Linganisha mbinu

	Log-Loss (aucdoti ya msalaba-entropi)	Alama ya Brier	F1-Score
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM	MCDM
Mwaka wa asili	1990s	1950	1979
Mwanzilishi	Information theory and machine learning literature	Glenn W. Brier	C. J. van Rijsbergen
Aina	Loss function	Loss function	Evaluation metric
Chanzo asilia	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Majina mbadala	Cross-Entropy Loss, Logloss	Mean Squared Probability Error	F-measure, Harmonic Mean
Zinazohusiana	3	3	5
Muhtasari	Log-loss measures the difference between predicted probabilities and actual labels, penalizing confident wrong predictions more than uncertain ones. It is a standard loss function in machine learning optimization and evaluates probabilistic classifier calibration.	The Brier score measures the mean squared difference between predicted probabilities and actual binary outcomes. It is a simple, interpretable metric for evaluating the accuracy of probabilistic predictions, particularly in weather forecasting and medical diagnosis.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.